Effects of airway pressure release ventilation on multi-organ injuries in severe acute respiratory distress syndrome pig models.

BACKGROUND: Extra-pulmonary multi-organ failure in patients with severe acute respiratory distress syndrome (ARDS) is a major cause of high mortality. Our purpose is to assess whether airway pressure release ventilation (APRV) causes more multi-organ damage than low tidal volume ventilation (LTV). METHODS: Twenty one pigs were randomized into control group (n = 3), ARDS group (n = 3), LTV group (n = 8) and APRV group (n = 7). Severe ARDS model was induced by repeated bronchial saline lavages. Pigs were ventilated and monitored continuously for 48 h. Respiratory data, hemodynamic data, serum inflammatory cytokines were collected throughout the study. Histological injury and apoptosis were assessed by two pathologists. RESULTS: After severe ARDS modeling, pigs in ARDS, LTV and APRV groups experienced significant hypoxemia and reduced lung static compliance (Cstat). Oxygenation recovered progressively after 16 h mechanical ventilation (MV) in LTV and APRV group. The results of the repeated measures ANOVA showed no statistical difference in the PaO2/FiO2 ratio between the APRV and LTV groups (p = 0.54). The Cstat showed a considerable improvement in APRV group with statistical significance (p < 0.01), which was significantly higher than in the LTV group since 16 h (p = 0.04). Histological injury scores showed a significantly lower injury score in the middle and lower lobes of the right lung in the APRV group compared to LTV (pmiddle = 0.04, plower = 0.01), and no significant increase in injury scores for extra-pulmonary organs, including kidney (p = 0.10), small intestine (p = 1.0), liver (p = 0.14, p = 0.13) and heart (p = 0.20). There were no significant differences in serum inflammatory cytokines between the two groups. CONCLUSION: In conclusion, in the experimental pig models of severe ARDS induced by repetitive saline lavage, APRV improved lung compliance with reduced lung injury of middle and lower lobes, and did not demonstrate more extra-pulmonary organ injuries as compared with LTV.

Machine learning model for predicting acute kidney injury progression in critically ill patients.

BACKGROUND: Acute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. Comparing to the patients with AKI stage 1/2, the patients with AKI stage 3 have higher in-hospital mortality and risk of progression to chronic kidney disease. The purpose of this study is to develop a prediction model that predict whether patients with AKI stage 1/2 will progress to AKI stage 3. METHODS: Patients with AKI stage 1/2, when they were first diagnosed with AKI in the Medical Information Mart for Intensive Care, were included. We used the Logistic regression and machine learning extreme gradient boosting (XGBoost) to build two models which can predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation, receiver operating characteristic curve, and precision-recall curves. RESULTS: We included 25,711 patients, of whom 2130 (8.3%) progressed to AKI stage 3. Creatinine, multiple organ failure syndromes were the most important in AKI progression prediction. The XGBoost model has a better performance than the Logistic regression model on predicting AKI stage 3 progression. Thus, we build a software based on our data which can predict AKI progression in real time. CONCLUSIONS: The XGboost model can better identify patients with AKI progression than Logistic regression model. Machine learning techniques may improve predictive modeling in medical research.

Association between body mass index and delirium incidence in critically ill patients: a retrospective cohort study based on the MIMIC-IV Database.

OBJECTIVES: Delirium is a form of brain dysfunction with high incidence and is associated with many negative outcomes in the intensive care unit. However, few studies have been large enough to reliably examine the associations between body mass index (BMI) and delirium, especially in critically ill patients. The objective of this study was to investigate the association between BMI and delirium incidence in critically ill patients. DESIGN: A retrospective cohort study. SETTING: Data were collected from the Medical Information Mart for Intensive Care-IV V2.0 Database consisting of critically ill participants between 2008 and 2019 at the Beth Israel Deaconess Medical Center in Boston. PARTICIPANTS: A total of 20 193 patients with BMI and delirium records were enrolled in this study and were divided into six groups. PRIMARY OUTCOME MEASURE: Delirium incidence. RESULTS: Generalised linear models and restricted cubic spline analysis were used to estimate the associations between BMI and delirium incidence. A total of 30.81% of the patients (6222 of 20 193) developed delirium in the total cohort. Compared with those in the healthy weight group, the patients in the different groups (underweight, overweight, obesity grade 1, obesity grade 2, obesity grade 3) had different relative risks (RRs): RR=1.10, 95% CI=1.02 to 1.19, p=0.011; RR=0.93, 95% CI=0.88 to 0.97, p=0.003; RR=0.88, 95% CI=0.83 to 0.94, p<0.001; RR=0.94, 95% CI=0.86 to 1.03, p=0.193; RR=1.14, 95% CI=1.03 to 1.25, p=0.010, respectively. For patients with or without adjustment variables, there was an obvious U-shaped relationship between BMI as a continuous variable and delirium incidence. CONCLUSION: BMI was associated with the incidence of delirium. Our results suggested that a BMI higher or lower than obesity grade 1 rather than the healthy weight in critically ill patients increases the risk of delirium incidence.

U-shaped association between serum albumin and pediatric intensive care unit mortality in critically ill children.

Introduction: The detailed association between albumin levels and mortality has not been studied in critically ill children. The aim of this study was to reveal an association between albumin levels in detail and mortality in critically ill children. Materials and methods: We retrospectively collected data from children admitted to four pediatric intensive care units (PICUs) in China between January 2015 and October 2020. Restricted cubic spline curves based on logistic regression models were generated to evaluate the detailed associations between serum albumin levels and PICU mortality. Threshold effect analysis was performed using two piecewise regression models. Results: The study included 9,123 children. The overall mortality was 5.3%. The detailed association between serum albumin levels and the risk of mortality followed a U-shape. The risk of mortality decreased with increasing serum albumin levels (OR = 0.919; 95% CI: 0.886, 0.954) in children with serum albumin levels < 43.2 g/L and increased with increasing serum albumin levels (OR = 1.174; 95% CI: 1.044, 1.316) in children with serum albumin levels ≥ 43.2 g/L. Conclusion: There was a U-shaped association between serum albumin levels and mortality in critically ill children in the PICU.

Performance of the PRISM I, PIM2, PELOD-2 and PRISM IV scoring systems in western China: a multicenter prospective study.

BACKGROUND: The aim of this study was to evaluate the performance of the four scoring tools in predicting mortality in pediatric intensive care units (PICUs) in western China. METHODS: This was a multicenter, prospective, cohort study conducted in six PICUs in western China. The performances of the scoring systems were evaluated based on both discrimination and calibration. Discrimination was assessed by calculating the area under the receiver operating characteristic curve (AUC) for each model. Calibration was measured across defined groups based on mortality risk using the Hosmer-Lemeshow goodness-of-fit test. RESULTS: A total of 2034 patients were included in this study, of whom 127 (6.2%) died. For the entire cohort, AUCs for Pediatric Risk of Mortality Score (PRISM) I, Pediatric Index of Mortality 2 (PIM2), Pediatric Logistic Organ Dysfunction Score-2 (PELOD-2) and PRISM IV were 0.88 [95% confidence interval (CI) 0.85-0.92], 0.84 (95% CI 0.80-0.88), 0.80 (95% CI 0.75-0.85), and 0.91 (95% CI 0.88-0.94), respectively. The Hosmer-Lemeshow goodness-of-fit Chi-square value was 12.71 (P = 0.12) for PRISM I, 4.70 (P = 0.79) for PIM2, 205.98 (P < 0.001) for PELOD-2, and 7.50 (P = 0.48) for PRISM IV [degree of freedom (df) = 8]. The standardized mortality ratios obtained with the PRISM I, PIM2, PELOD-2, and PRISM IV models were 0.87 (95% CI, 0.75-1.01), 0.97 (95% CI, 0.85-1.12), 1.74 (95% CI, 1.58-1.92), and 1.05 (95% CI, 0.92-1.21), respectively. CONCLUSIONS: PRISM IV performed best and can be used as a prediction tool in PICUs in Western China. However, PRISM IV needs to be further validated in NICUs.